CCMVI2085U Machine Learning for Predictive Analytics in Business @ CBS ISUP 2020
Course coordinator: Bowei Chen | Email: bc.acc@cbs.dk
Sometimes it is difficult to find a good balance between theory and practice. The ultimate goal of this course is to teach students how to use machine learning in business as well as tell them which models can be used and why they work.
By the end of this course students will be able to:
Python 3 + Jupyter notebook
I am a Lectuer (equivalent to US tenured Assistant Professor) at the Adam Smith Business School of University of Glasgow. I have broad research interest related to the applications of probabilistic modelling and deep learning in business, with special focuses on marketing and finance. You can find more details about my research and teaching at https://boweichen.github.io/
Fig by Yaser Abu-Mostafa
The term machine learning was coined in 1959 by Arthur Samuel, an American IBMer and pioneer in the field of computer gaming and artificial intelligence. He defined machine learning as a field of study that gives computers the ability to learn without being explicitly programmed. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.
https://www.pwc.com/us/en/services/consulting/analytics/big-decision-survey.html
O'Reilly conducted an anonymous survey on the tools successful data analysts and engineers use, and how those tool choices might relate to their salary. Source: https://www.oreilly.com/data/free/2014-datascience-salary-survey.csp
Tools within each cluster have high correlations, indicating that they are usually used as a combination. Although most data scientist respondents in this survey do not only use tools constraint to one of the above clusters, I do feel these clusters correspond well to the roles each data scientist plays in general:
Personally the tools I find most useful and interact with most frequently in daily work are from Cluster 2 + Cluster 3, and occasionally from Cluster 4 (D3, JavaScript). By Wenwen Tao, Quora Data Scientist [https://www.quora.com/What-tools-do-data-scientists-use]
Python is a modern, general-purpose, object-oriented, high-level programming language.
It is used for:
Pros
Cons
It is an interpreted language, might take up more CPU time. However, given the savings in programmer time (due to ease of learning), it might still be a good choice.
IDEs are software platforms that provide programmers and developers a comprehensive set of tools for software development in a single product. IDEs offer a central interface featuring all the tools a developer needs, including the following:
More information can be seen here: https://www.g2.com/categories/integrated-development-environments-ide
Installing Anaconda on different operating systems is very simple. You can find the detailed installation document from Anaconda webpage:
https://docs.anaconda.com/anaconda/install/
The following are the YouTube videos of installing Anaconda for Windows, Mac OS and Linux Ubuntu, respectively. You can also follow the videos to install Anaconda on your computer.